Density-Based Clustering Based on Hierarchical Density Estimates
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HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.
“HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.”
From Large language model trained on clinical oncology data predicts cancer progression (2025) · cites this paper as [54]
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- Authors
- Campello, R. J. G. B., Moulavi, D., & Sander, J.
- Affiliations
- University of Alberta (CA)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 7819
- Pages
- 160-172
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W151377110
- Cited by
- 2,680
- References
- 27
- Citations by year
- 2026: 413 · 2025: 497 · 2024: 404 · 2023: 337 · 2022: 271 · 2021: 253 · 2020: 181 · 2019: 148 · 2018: 84 · 2017: 50 · 2016: 24 · 2015: 9 · 2014: 9
- FWCI
- 9.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- ResearchOnline - JCU (James Cook University) (submittedVersion)
- Lecture Notes in Artificial Intelligence
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- density-based clustering, hierarchical clustering, density limit
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